Decreased gray matter volume in the right middle temporal gyrus associated with cognitive dysfunction in preeclampsia superimposed on chronic hypertension
Bibliographic record
Abstract
Introduction: The effects of preeclampsia superimposed on chronic hypertension (CHTN-PE) on the structure and function of the human brain are mostly unknown. The purpose of this study was to examine altered gray matter volume (GMV) and its correlation with cognitive function in pregnant healthy women, healthy non-pregnant individuals, and CHTN-PE patients. Methods: Twenty-five CHTN-PE patients, thirty-five pregnant healthy controls (PHC) and thirty-five non-pregnant healthy controls (NPHC) were included in this study and underwent cognitive assessment testing. A voxel-based morphometry (VBM) approach was applied to investigate variations in brain GMV among the three groups. Pearson's correlations between mean GMV and the Stroop color-word test (SCWT) scores were calculated. Results: Compared with the NPHC group, the PHC and CHTN-PE groups showed significantly decreased GMV in a cluster of the right middle temporal gyrus (MTG), and the GMV decrease was more significant in the CHTN-PE group. There were significant differences in the Montreal Cognitive Assessment (MoCA) and Stroop word scores among the three groups. Notably, the mean GMV values in the right MTG cluster were not only significantly negatively correlated with Stroop word and Stroop color scores but also significantly distinguished CHTN-PE patients from the NPHC and PHC groups in receiver operating characteristic curve analysis. Discussion: Pregnancy may cause a decrease in local GMV in the right MTG, and the GMV decrease is more significant in CHTN-PE patients. The right MTG affects multiple cognitive functions, and combined with the SCWT scores, it may explain the decline in speech motor function and cognitive flexibility in CHTN-PE patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".